FIG3_seismometerDasComparison.ipynb

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   "source": [
    "# Figure 3\n",
    "This figure compares the DAS sensitivity in a narrow band to the seismometer sensitivity for an earthquake record. The seismometer used is a colocated Trillium 3-axis Posthole Compact 120s sensor. The example is from the Fairbanks Array, where the seismometer is located at the intersection of two orthogonal DAS trenched cables. The earthquake path is aligned with one of the DAS trenches, meaning the horizontal components of the DAS array decompose into the transverse and radial components of ground motion, in theory. Mathematical rotation of the seismometer into this orientation (backazimuth=200 degrees) yields aligned sensor components. There is no vertical DAS component. \n",
    "\n",
    "DAS data must be scaled by the instrument manufacturing values used in the module \"scaleData\" below. This yields data in nm/seconds, to which we apply a time-derivative to compare data in units of acceleration.\n",
    "\n",
    "Nate Lindsey 2017"
   ]
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    "%matplotlib notebook\n",
    "import numpy as np\n",
    "from pylab import figure, cm\n",
    "from matplotlib.colors import LogNorm\n",
    "import obspy as obs\n",
    "from obspy import UTCDateTime\n",
    "import matplotlib.pyplot as plt\n",
    "import obspy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some useful modules to process DAS and inertial seismometer data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "def mat2st(mat):\n",
    "    import os\n",
    "    import matplotlib.pyplot as plt\n",
    "    import numpy as np\n",
    "    import h5py \n",
    "    import obspy as obs\n",
    "    from obspy.signal.filter import lowpass, bandpass\n",
    "    import scipy.integrate\n",
    "    \n",
    "    def streamInit():\n",
    "        st = obs.read()\n",
    "        tr = obs.Trace()\n",
    "        st.clear()\n",
    "        return st,tr\n",
    "\n",
    "    st,tr=streamInit()\n",
    "\n",
    "    with h5py.File(mat) as f:\n",
    "        try:\n",
    "            fh = [f[element[0]][:] for element in f['dsi_downsampled/fh']]\n",
    "            data = [f[element[0]][:] for element in f['dsi_downsampled/dat']]\n",
    "        except KeyError:\n",
    "            try:\n",
    "                fh = [f[element[0]][:] for element in f['dsi/fh']]\n",
    "                data = [f[element[0]][:] for element in f['dsi/dat']]\n",
    "            except KeyError:\n",
    "                try:\n",
    "                    fh = [f[element[0]][:] for element in f['DSI/fh']]\n",
    "                    data = [f[element[0]][:] for element in f['DSI/dat']]\n",
    "                except KeyError:\n",
    "                    fh = [f[element[0]][:] for element in f['dsi2/fh']]\n",
    "                    data = [f[element[0]][:] for element in f['dsi2/dat']]\n",
    "        delta=fh[7]\n",
    "        nChan = np.shape(data)[1]\n",
    "        nSamp = np.shape(data)[2]\n",
    "        for chan in np.arange(1,nChan,1):\n",
    "            tr = obs.Trace()\n",
    "            tr.stats.npts=nSamp\n",
    "            tr.stats.delta=delta\n",
    "            tr.stats.network='LBNL'\n",
    "            tr.stats.station=chan\n",
    "            tr.stats.location=''\n",
    "            tr.stats.channel=''\n",
    "            tr.stats.distance=chan\n",
    "            tr.data = data[0][chan][:]\n",
    "            st.append(tr)\n",
    "        return st\n",
    "    \n",
    "def scaleData(streamin):\n",
    "    a=1;\n",
    "    b=2048;\n",
    "    scale=a/b\n",
    "    gain = 116 # nm / radian\n",
    "    timefac=0.001; #seconds/timesample\n",
    "    streamout=streamin.copy()\n",
    "    for tr in streamout:\n",
    "        tr.data = tr.data*scale*gain/timefac\n",
    "    return streamout\n",
    "\n",
    "def bpStream(s,nc,lowcut,highcut):\n",
    "    from obspy.signal.filter import bandpass\n",
    "    import copy\n",
    "    import obspy\n",
    "    stream_bp = s.copy()\n",
    "    try:\n",
    "        for a in range(len(stream_bp)):\n",
    "            dt = stream_bp[a].stats.delta\n",
    "            stream_bp[a].data =  bandpass(stream_bp[a].data, lowcut, highcut, df=1/stream_bp[a].stats.delta, corners=nc, zerophase=False);\n",
    "    except:\n",
    "        dt = stream_bp.stats.delta\n",
    "        stream_bp.data =  bandpass(stream_bp.data, lowcut, highcut, df=1/stream_bp.stats.delta, corners=nc, zerophase=False);\n",
    "   \n",
    "    return stream_bp\n",
    "\n",
    "def stack_stream_specific(stream,startchannel,nchannels):\n",
    "    import copy\n",
    "    tracestack=stream[0].copy()\n",
    "    A=[a.data for a in stream[startchannel:startchannel+nchannels]]\n",
    "    tracestack.data = np.mean(np.array(A),0)\n",
    "    return tracestack\n",
    "def stack_stream_specific_median(stream,startchannel,nchannels):\n",
    "    import copy\n",
    "    tracestack=stream[0].copy()\n",
    "    A=[a.data for a in stream[startchannel:startchannel+nchannels]]\n",
    "    tracestack.data = np.median(np.array(A),0)\n",
    "    return tracestack\n",
    "\n",
    "def v2a(tr):\n",
    "    try:\n",
    "        tr2 = tr.copy()\n",
    "        tr2.data = np.gradient(tr2.data, tr2.stats.delta)\n",
    "        return tr2\n",
    "    except:\n",
    "        st2 = tr.copy()\n",
    "        D = [np.gradient(t.data, t.stats.delta) for t in st2]\n",
    "        st2.data = np.array(D)\n",
    "        return st2"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load seismometer data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "evt='Alaska Range'\n",
    "eq='160826'\n",
    "# bpath=\"/Users/njlindsey/Workspace/projectsBerkeley/DASeq/wd/FBX2016/DAS/160826/bb_sac_postIRR\"\n",
    "# path1='/Users/njlindsey/Workspace/projectsBerkeley/DASeq/ms/figs/fig1_fbx_road_gather/dsi_160826_12_24_Line1_20_30.mat'\n",
    "path1='/Users/njlindsey/Workspace/projectsBerkeley/DASeq/ms/figs/fig3_particle_velocity_comparison/data/das/160826_M38/Line1_channels_1_51.mat' \n",
    "# pathC='/Users/njlindsey/Workspace/projectsBerkeley/DASeq/ms/figs/fig1_fbx_road_gather/dsi_160826_12_24_LineC_100_110.mat'\n",
    "pathC='/Users/njlindsey/Workspace/projectsBerkeley/DASeq/ms/figs/fig3_particle_velocity_comparison/data/das/160826_M38/LineC_channels_1_180.mat'\n",
    "# pathR='/Users/njlindsey/Workspace/projectsBerkeley/DASeq/ms/figs/fig1_fbx_road_gather/dsi_160826_12_24_LineR_390_420.mat'\n",
    "st = UTCDateTime(\"2016-08-26T23:17:42.000000Z\")\n",
    "file_prefix='2016.239.23' ##Broadband"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
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   "source": [
    "bb5a_IRR = obs.Stream()\n",
    "bb5a_IRR += obs.read('/Users/njlindsey/Workspace/projectsBerkeley/DASeq/ms/figs/fig3_particle_velocity_comparison/data/broadband/BBC*%s_IRR.sac' % file_prefix)\n",
    "bb5a_IRR.rotate('NE->RT',back_azimuth=200); #360-17);\n",
    "# bb5a_IRR = bb5a_IRR[:].trim(starttime=st-4-60*0,endtime=st-4+60*1);\n",
    "bb5a_IRR = bb5a_IRR[:].trim(starttime=st-4-60*3,endtime=st-4+60*7);\n",
    "bb5a_IRR.resample(20);\n",
    "bb59_IRR = obs.Stream()\n",
    "bb59_IRR += obs.read('/Users/njlindsey/Workspace/projectsBerkeley/DASeq/ms/figs/fig3_particle_velocity_comparison/data/broadband/BBN*%s_IRR.sac' % file_prefix)\n",
    "bb59_IRR.rotate('NE->RT',back_azimuth=200); #360-17);\n",
    "# bb59_IRR = bb59_IRR[:].trim(starttime=st-4+60*0,endtime=st-4+60*1);\n",
    "bb59_IRR = bb59_IRR[:].trim(starttime=st-4-60*3,endtime=st-4+60*7);\n",
    "bb59_IRR.resample(20);\n",
    "baf7_IRR = obs.Stream()\n",
    "baf7_IRR += obs.read('/Users/njlindsey/Workspace/projectsBerkeley/DASeq/ms/figs/fig3_particle_velocity_comparison/data/broadband/BBS*%s_IRR.sac' % file_prefix)\n",
    "baf7_IRR.rotate('NE->RT',back_azimuth=200); #360-17);\n",
    "# baf7_IRR = baf7_IRR[:].trim(starttime=st-4+60*0,endtime=st-4+60*1);\n",
    "baf7_IRR = baf7_IRR[:].trim(starttime=st-4-60*3,endtime=st-4+60*7);\n",
    "baf7_IRR.resample(20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load DAS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:9: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "/Applications/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:12: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    }
   ],
   "source": [
    "craw = mat2st(pathC) #convert DAS data to stream\n",
    "oneraw = mat2st(path1)\n",
    "p = 11*59*60*20-1.25 #setup trim on time series to event time\n",
    "spm = 1200\n",
    "for i in range(len(craw)):\n",
    "    craw[i].data = craw[i].data[p+(8.5*spm):p+(18.5*spm)] #11.5,12.5 # trim das series\n",
    "    craw[i].stats.starttime=UTCDateTime(\"2016-08-26T23:17:38.000000Z\")  \n",
    "for i in range(len(oneraw)):\n",
    "    oneraw[i].data = oneraw[i].data[p+(8.5*spm):p+(18.5*spm)] #11.5,12.5\n",
    "    oneraw[i].stats.starttime=UTCDateTime(\"2016-08-26T23:17:38.000000Z\")  \n",
    "c = scaleData(craw) #scale das raw data to nm/s\n",
    "one = scaleData(oneraw);\n",
    "c.resample(20); #resample das data to 20hz, npts=12000, df= 10/12000= .00083333 hz (1200s)\n",
    "one.resample(20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the data in a range of frequency bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
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       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cstart=70\n",
    "onestart=10\n",
    "nchan=20\n",
    "\n",
    "for freq_index in range(6):\n",
    "    fmilist=[0.1,0.2,0.4,0.8,1.6,3.2]\n",
    "    fmalist=[0.2,0.4,0.8,1.6,3.2,6.4]\n",
    "\n",
    "    fmi=fmilist[freq_index]\n",
    "    fma=fmalist[freq_index]\n",
    "    nc=4\n",
    "\n",
    "    c_stack = stack_stream_specific_median(c,cstart,nchan)\n",
    "    one_stack = stack_stream_specific_median(one,onestart,nchan)\n",
    "\n",
    "    c_acc_stack = v2a(c_stack)\n",
    "    one_acc_stack = v2a(one_stack)\n",
    "\n",
    "    bc_ese_acc = v2a(bb5a_IRR[0])\n",
    "    bc_nne_acc = v2a(bb5a_IRR[1])\n",
    "    bc_z_acc = v2a(bb5a_IRR[2])\n",
    "    bn_ese_acc = v2a(bb59_IRR[0])\n",
    "    bn_nne_acc = v2a(bb59_IRR[1])\n",
    "    bn_z_acc = v2a(bb59_IRR[2])\n",
    "    bs_ese_acc = v2a(baf7_IRR[0])\n",
    "    bs_nne_acc = v2a(baf7_IRR[1])\n",
    "    bs_z_acc = v2a(baf7_IRR[2])\n",
    "\n",
    "    bc_ese_acc_bp = bpStream(bc_ese_acc,nc,fmi,fma)\n",
    "    bc_nne_acc_bp = bpStream(bc_nne_acc,nc,fmi,fma)\n",
    "    bc_z_acc_bp = bpStream(bc_z_acc,nc,fmi,fma)\n",
    "    bn_ese_acc_bp = bpStream(bn_ese_acc,nc,fmi,fma)\n",
    "    bn_nne_acc_bp = bpStream(bn_nne_acc,nc,fmi,fma)\n",
    "    bn_z_acc_bp = bpStream(bn_z_acc,nc,fmi,fma)\n",
    "    bs_ese_acc_bp = bpStream(bs_ese_acc,nc,fmi,fma)\n",
    "    bs_nne_acc_bp = bpStream(bs_nne_acc,nc,fmi,fma)\n",
    "    bs_z_acc_bp = bpStream(bs_z_acc,nc,fmi,fma)\n",
    "\n",
    "    one_acc_bp = bpStream(one_acc_stack,nc,fmi,fma)\n",
    "    c_acc_bp = bpStream(c_acc_stack,nc,fmi,fma)\n",
    "\n",
    "    # Figure a\n",
    "    fig,ax=plt.subplots(3,1,figsize=(12,9),sharex=True)\n",
    "    ax[0].plot(bn_ese_acc_bp.data,'gray',label='Broadband')\n",
    "    ax[1].plot(bn_nne_acc_bp.data,'gray',label='Broadband')\n",
    "    ax[2].plot(bn_z_acc_bp.data,'gray',label='Broadband')\n",
    "\n",
    "    ax[0].plot(-5.75*one_acc_bp.data,color='red',label='DAS')\n",
    "    ax[1].plot(-5.75*c_acc_bp.data,color='red',label='DAS')\n",
    "\n",
    "    ax[0].grid(which='both')\n",
    "    ax[1].grid(which='both')\n",
    "    ax[2].grid(which='both')\n",
    "    ax[0].set_title('Broadband vs DAS (BP p 1 n %.0f c %0.2f %0.2f Hz)' % (float(nc),fmi,fma))\n",
    "    ax[0].set_xlim([3400,5000])\n",
    "    ax[0].set_ylabel('Transverse [$\\mu$m/s$^{2}]$',fontsize=14)\n",
    "    ax[1].set_ylabel('Radial [$\\mu$m/s$^{2}]$',fontsize=14)\n",
    "    ax[2].set_ylabel('Vertical [$\\mu$m/s$^{2}]$',fontsize=14)\n",
    "    ax[2].set_xticklabels(np.arange(0,80,10))\n",
    "    ax[0].set_yticklabels([int(a/1000) for a in ax[0].get_yticks()])\n",
    "    ax[1].set_yticklabels([int(a/1000) for a in ax[1].get_yticks()])\n",
    "    ax[2].set_yticklabels([int(a/1000) for a in ax[2].get_yticks()])\n",
    "\n",
    "    ax[2].set_xlabel('Time [sec.]',fontsize=14)\n",
    "    ax[0].legend(loc='best',fontsize=10)\n",
    "    ax[1].legend(loc='best',fontsize=10)\n",
    "    ax[2].legend(loc='best',fontsize=10)\n",
    "    plt.savefig('acceleration_comparison_%.1f_%.1fHz_median.eps' % (fmi,fma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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